AI/ML Research Engineer
Peraton · Red Bank, NJ, US
About this role
## AI/ML Research Engineer ### Responsibilities - Lead the design and implementation of the machine learning core for extensive R&D into **generative models**. - Own the **research-to-code** path for model generation, planning, and optimization, including: - Formulating models - Building **training and inference pipelines** - Integrating with **synthetic and laboratory datasets** - Delivering **trained, containerized frameworks** - Design, implement, and train **causal graph models** and **planning algorithms**. - Architect approaches to **multi-objective optimization**. - Build and maintain ML infrastructure, including: - Training/inference pipelines - Experiment tracking - Model versioning - Dataset loaders (synthetic + real) - Objective and interface packaging - Package trained models for inference as **containerized components** that conform to the specified API; coordinate with the Lead System Integrator for regular code drops and major revisions. - Contribute to **synthetic data generation and simulation** so training data covers the objective space the models must generalize across. - Present research results at program design reviews, site visits, and PI workshops; author technical sections of monthly status reports and design documentation. - Coordinate with academic subcontractor researchers on shared model components. ### Qualifications **Required** - **5+ years** of experience and an **MS or PhD** in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or related. - **2+ years** of applied ML research experience in at least one of: - Causal inference / structural causal models - Bayesian networks / probabilistic graphical models - Probabilistic programming (Pyro, NumPyro, Stan, PyMC, or similar) - Learned optimization and planning - Strong **Python** software engineering skills with production-quality **PyTorch** (custom model implementation, training loop design, GPU performance profiling). - Ability to translate research prototypes into **maintainable, tested, containerized code** that runs unattended in an environment the developer does not control. - Working knowledge of **multi-objective and constrained optimization** (evolutionary, Bayesian, gradient-based, or combinatorial) applied to structured design spaces. - Experience with **experiment management**, reproducible ML pipelines, and dataset versioning on a multi-person research team. - Ability to read and implement methods from current research literature and clearly communicate model behavior/limitations to non-ML engineers and government stakeholders. - **US Citizenship**. **Desired** - Prior work on **IARPA, DARPA, or similar** government research programs. - Background in **communications, digital signal processing, or information theory** (e.g., modulation, coding, channel models). - Experience with **neuro-symbolic methods**, program synthesis, or graph generation models (GNNs, autoregressive graph generators, diffusion over graphs). - Familiarity with **symbolic regression** (e.g., PySR) or generating executable code / DSL artifacts from learned models. - Experience with **hardware-constrained inference** (compression, quantization, embedded/edge deployment). - Publications in causal inference, probabilistic ML, or ML for wireless communications. - Ability to obtain **Secret security clearance**. ### Company **Peraton** — next-generation national security company and mission capability integrator. ### Targe
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